We have developed computer methods to predict protein three-dimensional structure by recognition of folding motif. A protein's sequence is "threaded" through alternative backbone structures in a database, and the conformations most stabilized by that sequence are identified. The work has focused on three areas: 1) derivation of a rapidly-evaluated empirical free energy function, 2) testing of algorithms for fast threading through gapped "core" motifs, and 3) identification of core sub-structures to be included in the database. This year we have shown that an energy function based on residue contact potentials can identify the correct core motif and alignment among many billions of alternatives, a specificity sufficient for many prediction problems. We have developed statistical corrections for effects of sequence composition and length, which otherwise reduce motif-recognition specificity. We have tested a fast-threading algorithm based on a monte carlo technique, and found that it is capable of identifying optimal alignments of sequence and fixed-size core motifs in a few seconds, in all cases where this optimum can be determined with certainty by enumeration. To define a database of folding motifs we have extracted from the Protein Data Bank substructures consisting of major helices and beta-strands, using an algorithm resistant to local distortions and/or coordinate imprecision. Each defines a family of related core motifs, when threaded with a monte carlo algorithm that allows deletions and/or changes in size of individual secondary structure elements. Testing of this adaptive threading algorithm and its associated database is in progress. The significance of this research is that these methods may allow automated search of a folding motif database, predicting substructure conformations in proteins which may share little or no homology with proteins in the crystallographic database.